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Orientador(es)
Resumo(s)
In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.
Descrição
Palavras-chave
Cluster analysis Finite mixture models EM-MML algorithm Feature selection Categorical features
Contexto Educativo
Citação
Silvestre, Cláudia; Cardoso, Margarida; Figueiredo, Mário - Clustering and Selecting Categorical Features. In Progress in Artificial Intelligence: Lecture Notes in Computer Science: XVI PORTUGUESE CONFERENCE ON ARTIFICIAL INTELLIGENCE – EPIA 2013, Angra do Heroísmo, (Açores), 09-12 Septemnber 2013, (Volume 8154, 2013, pp 331-342)
Editora
Springer